Collective operation protocol selection in a parallel computer

ABSTRACT

Collective operation protocol selection in a parallel computer that includes compute nodes may be carried out by calling a collective operation with operating parameters; selecting a protocol for executing the operation and executing the operation with the selected protocol. Selecting a protocol includes: iteratively, until a prospective protocol meets predetermined performance criteria: providing, to a protocol performance function for the prospective protocol, the operating parameters; determining whether the prospective protocol meets predefined performance criteria by evaluating a predefined performance fit equation, calculating a measure of performance of the protocol for the operating parameters; determining that the prospective protocol meets predetermined performance criteria and selecting the protocol for executing the operation only if the calculated measure of performance is greater than a predefined minimum performance threshold.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for collective operation protocolselection in a parallel computer.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same task (split up and specially adapted) on multiple processorsin order to obtain results faster. Parallel computing is based on thefact that the process of solving a problem usually can be divided intosmaller tasks, which may be carried out simultaneously with somecoordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing tasks via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In sucha manner, a torus network lends itself to point to point operations. Ina tree network, the nodes typically are connected into a binary tree:each node has a parent, and two children (although some nodes may onlyhave zero children or one child, depending on the hardwareconfiguration). Although a tree network typically is inefficient inpoint to point communication, a tree network does provide high bandwidthand low latency for certain collective operations, message passingoperations where all compute nodes participate simultaneously, such as,for example, an allgather operation. In computers that use a torus and atree network, the two networks typically are implemented independentlyof one another, with separate routing circuits, separate physical links,and separate message buffers.

Compute nodes in a parallel computer may also be organized into anoperational group to carry out collective parallel operations.Collective operations are implemented with data communications among thecompute nodes of an operational group. Collective operations are thosefunctions that involve all the compute nodes of an operational group. Acollective operation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group ofcompute nodes. A ‘broadcast’ is an example of a collective operation formoving data among compute nodes of an operational group. A ‘reduce’operation is an example of a collective operation that executesarithmetic or logical functions on data distributed among the computenodes of an operational group. Protocols for collective operations maytuned or optimized for particular operating parameters—parameters withinwhich the collective operation executes. Examples of such parameters maybe a type of logical, or arithmetic function to execute, data types,data size, number of nodes, and the like. Collective operation protocolsmay optimized with respect to particular sets of operating parameters inthat the protocols may be more efficient than other protocols, consumeless power during execution than other protocols, utilize fewerresources that other protocols, executed more quickly than otherprotocols, and so on as will occur to readers of skill in the art.Increasing accuracy of selecting an optimized protocol for collectiveoperations, therefore, may be beneficial to data processing in aparallel computing system.

SUMMARY OF THE INVENTION

Methods, apparatus, and products for collective operation protocolselection in a parallel computer are described in this specification.The parallel computer includes a number of compute nodes. Suchcollective operation protocol selection includes: calling a collectiveoperation with one or more operating parameters, selecting one of anumber of protocols for executing the collective operation, andexecuting the collective operation with the selected protocol. Inembodiments of the present invention, selecting one of the protocols iscarried out iteratively, for each protocol beginning with a firstprospective protocol until a prospective protocol meets predeterminedperformance criteria and includes: providing, to a protocol performancefunction for the prospective protocol, the operating parameters of thecollective operation; determining, by the performance function, whetherthe prospective protocol meets predefined performance criteria for theoperating parameters, including evaluating, with the operatingparameters, a predefined performance fit equation for the prospectiveprotocol and calculating a measure of performance of the prospectiveprotocol for the operating parameters; and determining that theprospective protocol meets predetermined performance criteria andselecting the prospective protocol as the protocol for executing thecollective operation only if the calculated measure of performance isgreater than a predefined minimum performance threshold.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for collective operation protocolselection in a parallel computer according to embodiments of the presentinvention.

FIG. 2 sets forth a block diagram of an example compute node (102)useful in a parallel computer capable of collective operation protocolselection according to embodiments of the present invention.

FIG. 3A sets forth a block diagram of an example Point-To-Point Adapteruseful in systems for collective operation protocol selection in aparallel computer according to embodiments of the present invention.

FIG. 3B sets forth a block diagram of an example Global CombiningNetwork Adapter useful in systems for collective operation protocolselection in a parallel computer according to embodiments of the presentinvention.

FIG. 4 sets forth a line drawing illustrating an example datacommunications network optimized for point-to-point operations useful insystems capable of collective operation protocol selection in a parallelcomputer according to embodiments of the present invention.

FIG. 5 sets forth a line drawing illustrating an example globalcombining network useful in systems capable of collective operationprotocol selection in a parallel computer according to embodiments ofthe present invention.

FIG. 6 sets forth a flow chart illustrating an example method forcollective operation protocol selection in a parallel computer accordingto embodiments of the present invention.

FIG. 7 sets forth a flow chart illustrating a further example method forcollective operation protocol selection in a parallel computer accordingto embodiments of the present invention.

FIG. 8 sets forth a flow chart illustrating a further example method forcollective operation protocol selection in a parallel computer accordingto embodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary methods, apparatus, and products for collective operationprotocol selection in a parallel computer in accordance with the presentinvention are described with reference to the accompanying drawings,beginning with FIG. 1. FIG. 1 illustrates an exemplary system forcollective operation protocol selection in a parallel computer accordingto embodiments of the present invention. The system of FIG. 1 includes aparallel computer (100), non-volatile memory for the computer in theform of a data storage device (118), an output device for the computerin the form of a printer (120), and an input/output device for thecomputer in the form of a computer terminal (122).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a global combining network (106)which is optimized for collective operations using a binary tree networktopology, and a point-to-point network (108), which is optimized forpoint-to-point operations using a torus network topology. The globalcombining network (106) is a data communications network that includesdata communications links connected to the compute nodes (102) so as toorganize the compute nodes (102) as a binary tree. Each datacommunications network is implemented with data communications linksamong the compute nodes (102). The data communications links providedata communications for parallel operations among the compute nodes(102) of the parallel computer (100).

The compute nodes (102) of the parallel computer (100) are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on the parallel computer (100). Eachoperational group (132) of compute nodes is the set of compute nodesupon which a collective parallel operation executes. Each compute nodein the operational group (132) is assigned a unique rank that identifiesthe particular compute node in the operational group (132). Collectiveoperations are implemented with data communications among the computenodes of an operational group. Collective operations are those functionsthat involve all the compute nodes of an operational group (132). Acollective operation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group (132) ofcompute nodes. Such an operational group (132) may include all thecompute nodes (102) in a parallel computer (100) or a subset all thecompute nodes (102). Collective operations are often built aroundpoint-to-point operations. A collective operation requires that allprocesses on all compute nodes within an operational group (132) callthe same collective operation with matching arguments. A ‘broadcast’ isan example of a collective operation for moving data among compute nodesof an operational group. A ‘reduce’ operation is an example of acollective operation that executes arithmetic or logical functions ondata distributed among the compute nodes of an operational group (132).An operational group (132) may be implemented as, for example, an MPI‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallelcommunications library, a module of computer program instructions fordata communications on parallel computers. Examples of prior-artparallel communications libraries that may be improved for use insystems configured according to embodiments of the present inventioninclude MPI and the ‘Parallel Virtual Machine’ (‘PVM’) library. PVM wasdeveloped by the University of Tennessee, The Oak Ridge NationalLaboratory and Emory University. MPI is promulgated by the MPI Forum, anopen group with representatives from many organizations that define andmaintain the MPI standard. MPI at the time of this writing is a de factostandard for communication among compute nodes running a parallelprogram on a distributed memory parallel computer. This specificationsometimes uses MPI terminology for ease of explanation, although the useof MPI as such is not a requirement or limitation of the presentinvention.

Some collective operations have a single originating or receivingprocess running on a particular compute node in an operational group(132). For example, in a ‘broadcast’ collective operation, the processon the compute node that distributes the data to all the other computenodes is an originating process. In a ‘gather’ operation, for example,the process on the compute node that received all the data from theother compute nodes is a receiving process. The compute node on whichsuch an originating or receiving process runs is referred to as alogical root.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. The interfaces forthese collective operations are defined in the MPI standards promulgatedby the MPI Forum. Algorithms for executing collective operations,however, are not defined in the MPI standards. In a broadcast operation,all processes specify the same root process, whose buffer contents willbe sent. Processes other than the root specify receive buffers. Afterthe operation, all buffers contain the message from the root process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. In a scatter operation, the logical root dividesdata on the root into segments and distributes a different segment toeach compute node in the operational group (132). In scatter operation,all processes typically specify the same receive count. The sendarguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer is divided and dispersed to all processes (including the processon the logical root). Each compute node is assigned a sequentialidentifier termed a ‘rank.’ After the operation, the root has sentsendcount data elements to each process in increasing rank order. Rank 0receives the first sendcount data elements from the send buffer. Rank 1receives the second sendcount data elements from the send buffer, and soon.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduction operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from compute node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process' receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following predefinedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical and MPI_BAND bitwise and MPI_LOR logical or MPI_BOR bitwise orMPI_LXOR logical exclusive or MPI_BXOR bitwise exclusive or

In addition to compute nodes, the parallel computer (100) includesinput/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102)through the global combining network (106). The compute nodes (102) inthe parallel computer (100) may be partitioned into processing sets suchthat each compute node in a processing set is connected for datacommunications to the same I/O node. Each processing set, therefore, iscomposed of one I/O node and a subset of compute nodes (102). The ratiobetween the number of compute nodes to the number of I/O nodes in theentire system typically depends on the hardware configuration for theparallel computer (102). For example, in some configurations, eachprocessing set may be composed of eight compute nodes and one I/O node.In some other configurations, each processing set may be composed ofsixty-four compute nodes and one I/O node. Such example are forexplanation only, however, and not for limitation. Each I/O nodeprovides I/O services between compute nodes (102) of its processing setand a set of I/O devices. In the example of FIG. 1, the I/O nodes (110,114) are connected for data communications I/O devices (118, 120, 122)through local area network (‘LAN’) (130) implemented using high-speedEthernet.

The parallel computer (100) of FIG. 1 also includes a service node (116)coupled to the compute nodes through one of the networks (104). Servicenode (116) provides services common to pluralities of compute nodes,administering the configuration of compute nodes, loading programs intothe compute nodes, starting program execution on the compute nodes,retrieving results of program operations on the compute nodes, and soon. Service node (116) runs a service application (124) and communicateswith users (128) through a service application interface (126) that runson computer terminal (122).

The parallel computer (100) of FIG. 1 operates generally for collectiveoperation protocol selection with a number of compute nodes inaccordance with embodiments of the present invention. For clarity ofexplanation, compute node (102 a) is depicted here as an example of onecompute node in the operational group. Readers of skill in the art willrecognize that other compute nodes in the operational group will havesimilar ranks, data elements, data structures, processes, functions, andwill operate in a similar manner as the example compute node (102 a).

Compute node (102 a) includes a rank (212)—a process in an MPIcommunicator, the operational group (132). The rank (212), calls acollective operation (220) with one or more operating parameters (214).Operating parameters as the term is used in this specification may beany parameter passed to a collective operation for purposes of executingthat collective operation. Examples of such operating parameters includemessage size, data type, number and identifier of target nodes, and soon as will occur to readers of skill in the art.

The collective operation (220)—some other module of computer programinstructions not shown here—may then select one of a number of protocols(222) for executing the collective operation. Such a selection iscarried out iteratively in accordance with embodiments of the presentinvention, for each protocol (222) beginning with a first prospectiveprotocol until a prospective protocol meets predetermined performancecriteria. Predetermined performance criteria is any value that may bepredetermined to represent an acceptable level of ‘performance.’Examples of various performance criteria types include speed ofexecution, number of resources utilized in execution, and so on as willoccur to readers of skill in the art.

Each iteration of protocol selection includes: providing, to a protocolperformance function (228) for the prospective protocol, the operatingparameters (214) of the collective operation and determining, by theperformance function (228), whether the prospective protocol (222) meetspredefined performance criteria for the operating parameters. Aprotocol's performance function is a function, or subroutine of computerprogram instructions, that when executed determines whether the protocolwhether the protocol, for the particular set of operating parameters,will produce an optimized performance result. In some embodiments, forexample, the return from a protocol's performance function is a ‘goodfit’ or ‘bad fit’ result.

In the example of FIG. 1, the collective operation identifies and callsthe protocol performance function (228) in dependence upon metadata(224) for—or associated with—the prospective protocol. That is, eachprotocol available for selection is described in the example of FIG. 1by metadata. Such metadata may describe many different attributes of theprotocol. In embodiments of the present invention, for example, themetadata (224) of each protocol may include a pointer to the protocol'sperformance function (228).

The performance function (228) may determine whether the whether theprospective protocol (222) meets predefined performance criteria for theoperating parameters by evaluating, with the operating parameters (214),a predefined performance fit equation (230) for the prospectiveprotocol, thereby calculating a measure of performance of theprospective protocol for the operating parameters. Performance of eachprotocol relative to operating parameter sets in the example of FIG. 1,through that protocol's performance function (228), is described,specified, or defined by an fit equation. Such a fit equation may begenerated through linear, quadratic, or quartic regression analysis ofpreviously measured performance data. That is, each protocol may beexecuted a number of times with a number of different operatingparameter sets, measuring and storing performance data generated on eachexecution. Regression analysis may then be performed with the measuredand stored performance data to establish a fit equation thatapproximately describes the performance qualities of the protocol. Eachfit equation then returns a calculated measure of performance (218) fora particular set of operating parameters.

During real-time protocol selection, if the calculated measure ofperformance (218) is greater than a predefined minimum performancethreshold—the performance criteria (216) in the example of FIG. 1—thecollective protocol (220) determines that the prospective protocol meetspredetermined performance criteria and selects the prospective protocolas the protocol for executing the collective operation. If thecalculated measure of performance (218) is not greater than a predefinedminimum performance threshold, the selection process proceeds to asubsequent iteration, with another prospective protocol. Once selected,the collective operation (220) executes with the selected protocol.

The arrangement of nodes, networks, and I/O devices making up theexample apparatus illustrated in FIG. 1 are for explanation only, notfor limitation of the present invention. Systems configured forcollective operation protocol selection in a parallel computer accordingto embodiments of the present invention may include additional nodes,networks, devices, and architectures, not shown in FIG. 1, as will occurto those of skill in the art. The parallel computer (100) in the exampleof FIG. 1 includes sixteen compute nodes (102); parallel computersconfigured for collective operation protocol selection according toembodiments of the present invention sometimes include thousands ofcompute nodes. In addition to Ethernet (174) and JTAG (104), networks insuch data processing systems may support many data communicationsprotocols including for example TCP (Transmission Control Protocol), IP(Internet Protocol), and others as will occur to those of skill in theart. Various embodiments of the present invention may be implemented ona variety of hardware platforms in addition to those illustrated in FIG.1.

Collective operation protocol selection according to embodiments of thepresent invention is generally implemented on a parallel computer thatincludes a plurality of compute nodes organized for collectiveoperations through at least one data communications network. In fact,such computers may include thousands of such compute nodes. Each computenode is in turn itself a kind of computer composed of one or morecomputer processing cores, its own computer memory, and its owninput/output adapters. For further explanation, therefore, FIG. 2 setsforth a block diagram of an example compute node (102) useful in aparallel computer capable of collective operation protocol selectionaccording to embodiments of the present invention. The compute node(102) of FIG. 2 includes a plurality of processing cores (165) as wellas RAM (156). The processing cores (165) of FIG. 2 may be configured onone or more integrated circuit dies. Processing cores (165) areconnected to RAM (156) through a high-speed memory bus (155) and througha bus adapter (194) and an extension bus (168) to other components ofthe compute node. Stored in RAM (156) is an application program (159), amodule of computer program instructions that carries out parallel,user-level data processing using parallel algorithms.

Also stored RAM (156) is a parallel communications library (161), alibrary of computer program instructions that carry out parallelcommunications among compute nodes, including point-to-point operationsas well as collective operations. A library of parallel communicationsroutines may be developed from scratch for use in systems according toembodiments of the present invention, using a traditional programminglanguage such as the C programming language, and using traditionalprogramming methods to write parallel communications routines that sendand receive data among nodes on two independent data communicationsnetworks. Alternatively, existing prior art libraries may be improved tooperate according to embodiments of the present invention. Examples ofprior-art parallel communications libraries include the ‘Message PassingInterface’ (‘MPI’) library and the ‘Parallel Virtual Machine’ (‘PVM’)library.

Also stored in ram is a rank (212), a process in an MPI communicator.The rank (212) and the parallel communications library (161), whenexecuted, causes the compute node (102) to operate generally forcollective operation protocol selection in accordance with embodimentsof the present invention. The rank (212), in the example of FIG. 2,calls a collective operation (220) with one or more operating parameters(214) and the parallel communications library (161) commence protocol(222) selection for the collective operation (220). The parallelcommunication library (161) may select one of the protocols (222) forexecuting the collective operation in an iterative processes, beginningwith a first prospective protocol and continuing until a prospectiveprotocol meets predetermined performance criteria. Each iteration in theselection process includes providing, to a protocol performance function(228) for the prospective protocol referenced by a pointer (226) storedin the protocol's metadata (224), the operating parameters (214) of thecollective operation (220). The performance function (228). Theperformance function (228) determines whether the prospective protocolmeets predefined performance criteria (216) for the operating parameters(214), by evaluating, with the operating parameters (214), a predefinedperformance fit equation (230) for the prospective protocol (222),thereby calculating a measure of performance (218) of the prospectiveprotocol (222) for the operating parameters (214). If the calculatedmeasure of performance (218) is greater than a predefined minimumperformance threshold—set forth in the performance criteria (216)—theperformance function determines that the prospective protocol meets thepredetermined performance criteria (216) and selects the prospectiveprotocol (222) as the protocol for executing the collective operation(220). The parallel communications library (161) then executes thecollective operation with the selected protocol.

Also stored in RAM (156) is an operating system (162), a module ofcomputer program instructions and routines for an application program'saccess to other resources of the compute node. It is typical for anapplication program and parallel communications library in a computenode of a parallel computer to run a single thread of execution with nouser login and no security issues because the thread is entitled tocomplete access to all resources of the node. The quantity andcomplexity of tasks to be performed by an operating system on a computenode in a parallel computer therefore are smaller and less complex thanthose of an operating system on a serial computer with many threadsrunning simultaneously. In addition, there is no video I/O on thecompute node (102) of FIG. 2, another factor that decreases the demandson the operating system. The operating system (162) may therefore bequite lightweight by comparison with operating systems of generalpurpose computers, a pared down version as it were, or an operatingsystem developed specifically for operations on a particular parallelcomputer. Operating systems that may usefully be improved, simplified,for use in a compute node include UNIX™, Linux™, Windows XP™, AIX™,IBM's i5/OS™, and others as will occur to those of skill in the art.

The example compute node (102) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters useful in apparatus capable of collectiveoperation protocol selection in a parallel computer include modems forwired communications, Ethernet (IEEE 802.3) adapters for wired networkcommunications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (102)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 include a JTAGSlave circuit (176) that couples example compute node (102) for datacommunications to a JTAG Master circuit (178). JTAG is the usual nameused for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also useful asa mechanism for debugging embedded systems, providing a convenientalternative access point into the system. The example compute node ofFIG. 2 may be all three of these: It typically includes one or moreintegrated circuits installed on a printed circuit board and may beimplemented as an embedded system having its own processing core, itsown memory, and its own I/O capability. JTAG boundary scans through JTAGSlave (176) may efficiently configure processing core registers andmemory in compute node (102) for use in dynamically reassigning aconnected node to a block of compute nodes useful in systems forcollective operation protocol selection in a parallel computer accordingto embodiments of the present invention.

The data communications adapters in the example of FIG. 2 include aPoint-To-Point Network Adapter (180) that couples example compute node(102) for data communications to a network (108) that is optimal forpoint-to-point message passing operations such as, for example, anetwork configured as a three-dimensional torus or mesh. ThePoint-To-Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186).

The data communications adapters in the example of FIG. 2 include aGlobal Combining Network Adapter (188) that couples example compute node(102) for data communications to a global combining network (106) thatis optimal for collective message passing operations such as, forexample, a network configured as a binary tree. The Global CombiningNetwork Adapter (188) provides data communications through threebidirectional links for each global combining network (106) that theGlobal Combining Network Adapter (188) supports. In the example of FIG.2, the Global Combining Network Adapter (188) provides datacommunications through three bidirectional links for global combiningnetwork (106): two to children nodes (190) and one to a parent node(192).

The example compute node (102) includes multiple arithmetic logic units(‘ALUs’). Each processing core (165) includes an ALU (166), and aseparate ALU (170) is dedicated to the exclusive use of the GlobalCombining Network Adapter (188) for use in performing the arithmetic andlogical functions of reduction operations, including an allreduceoperation. Computer program instructions of a reduction routine in aparallel communications library (161) may latch an instruction for anarithmetic or logical function into an instruction register (169). Whenthe arithmetic or logical function of a reduction operation is a ‘sum’or a ‘logical OR,’ for example, the collective operations adapter (188)may execute the arithmetic or logical operation by use of the ALU (166)in the processing core (165) or, typically much faster, by use of thededicated ALU (170) using data provided by the nodes (190, 192) on theglobal combining network (106) and data provided by processing cores(165) on the compute node (102).

Often when performing arithmetic operations in the global combiningnetwork adapter (188), however, the global combining network adapter(188) only serves to combine data received from the children nodes (190)and pass the result up the network (106) to the parent node (192).Similarly, the global combining network adapter (188) may only serve totransmit data received from the parent node (192) and pass the data downthe network (106) to the children nodes (190). That is, none of theprocessing cores (165) on the compute node (102) contribute data thatalters the output of ALU (170), which is then passed up or down theglobal combining network (106). Because the ALU (170) typically does notoutput any data onto the network (106) until the ALU (170) receivesinput from one of the processing cores (165), a processing core (165)may inject the identity element into the dedicated ALU (170) for theparticular arithmetic operation being perform in the ALU (170) in orderto prevent alteration of the output of the ALU (170). Injecting theidentity element into the ALU, however, often consumes numerousprocessing cycles. To further enhance performance in such cases, theexample compute node (102) includes dedicated hardware (171) forinjecting identity elements into the ALU (170) to reduce the amount ofprocessing core resources required to prevent alteration of the ALUoutput. The dedicated hardware (171) injects an identity element thatcorresponds to the particular arithmetic operation performed by the ALU.For example, when the global combining network adapter (188) performs abitwise OR on the data received from the children nodes (190), dedicatedhardware (171) may inject zeros into the ALU (170) to improveperformance throughout the global combining network (106).

For further explanation, FIG. 3A sets forth a block diagram of anexample Point-To-Point Adapter (180) useful in systems for collectiveoperation protocol selection in a parallel computer according toembodiments of the present invention. The Point-To-Point Adapter (180)is designed for use in a data communications network optimized forpoint-to-point operations, a network that organizes compute nodes in athree-dimensional torus or mesh. The Point-To-Point Adapter (180) in theexample of FIG. 3A provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). The Point-To-Point Adapter (180) of FIG. 3A alsoprovides data communication along a y-axis through four unidirectionaldata communications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). ThePoint-To-Point Adapter (180) of FIG. 3A also provides data communicationalong a z-axis through four unidirectional data communications links, toand from the next node in the −z direction (186) and to and from thenext node in the +z direction (185).

For further explanation, FIG. 3B sets forth a block diagram of anexample Global Combining Network Adapter (188) useful in systems forcollective operation protocol selection in a parallel computer accordingto embodiments of the present invention. The Global Combining NetworkAdapter (188) is designed for use in a network optimized for collectiveoperations, a network that organizes compute nodes of a parallelcomputer in a binary tree. The Global Combining Network Adapter (188) inthe example of FIG. 3B provides data communication to and from childrennodes of a global combining network through four unidirectional datacommunications links (190), and also provides data communication to andfrom a parent node of the global combining network through twounidirectional data communications links (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in systems capable of collectiveoperation protocol selection in a parallel computer according toembodiments of the present invention. In the example of FIG. 4, dotsrepresent compute nodes (102) of a parallel computer, and the dottedlines between the dots represent data communications links (103) betweencompute nodes. The data communications links are implemented withpoint-to-point data communications adapters similar to the oneillustrated for example in FIG. 3A, with data communications links onthree axis, x, y, and z, and to and fro in six directions +x (181), −x(182), +y (183), −y (184), +z (185), and −z (186). The links and computenodes are organized by this data communications network optimized forpoint-to-point operations into a three dimensional mesh (105). The mesh(105) has wrap-around links on each axis that connect the outermostcompute nodes in the mesh (105) on opposite sides of the mesh (105).These wrap-around links form a torus (107). Each compute node in thetorus has a location in the torus that is uniquely specified by a set ofx, y, z coordinates. Readers will note that the wrap-around links in they and z directions have been omitted for clarity, but are configured ina similar manner to the wrap-around link illustrated in the x direction.For clarity of explanation, the data communications network of FIG. 4 isillustrated with only 27 compute nodes, but readers will recognize thata data communications network optimized for point-to-point operationsfor use in collective operation protocol selection in a parallelcomputer in accordance with embodiments of the present invention maycontain only a few compute nodes or may contain thousands of computenodes. For ease of explanation, the data communications network of FIG.4 is illustrated with only three dimensions, but readers will recognizethat a data communications network optimized for point-to-pointoperations for use in collective operation protocol selection in aparallel computer in accordance with embodiments of the presentinvention may in facet be implemented in two dimensions, fourdimensions, five dimensions, and so on. Several supercomputers now usefive dimensional mesh or torus networks, including, for example, IBM'sBlue Gene Q™.

For further explanation, FIG. 5 sets forth a line drawing illustratingan example global combining network (106) useful in systems capable ofcollective operation protocol selection in a parallel computer accordingto embodiments of the present invention. The example data communicationsnetwork of FIG. 5 includes data communications links (103) connected tothe compute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with global combining network adapters similar tothe one illustrated for example in FIG. 3B, with each node typicallyproviding data communications to and from two children nodes and datacommunications to and from a parent node, with some exceptions. Nodes inthe global combining network (106) may be characterized as a physicalroot node (202), branch nodes (204), and leaf nodes (206). The physicalroot (202) has two children but no parent and is so called because thephysical root node (202) is the node physically configured at the top ofthe binary tree. The leaf nodes (206) each has a parent, but leaf nodeshave no children. The branch nodes (204) each has both a parent and twochildren. The links and compute nodes are thereby organized by this datacommunications network optimized for collective operations into a binarytree (106). For clarity of explanation, the data communications networkof FIG. 5 is illustrated with only 31 compute nodes, but readers willrecognize that a global combining network (106) optimized for collectiveoperations for use in collective operation protocol selection in aparallel computer in accordance with embodiments of the presentinvention may contain only a few compute nodes or may contain thousandsof compute nodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifies atask or process that is executing a parallel operation according toembodiments of the present invention. Using the rank to identify a nodeassumes that only one such task is executing on each node. To the extentthat more than one participating task executes on a single node, therank identifies the task as such rather than the node. A rank uniquelyidentifies a task's location in the tree network for use in bothpoint-to-point and collective operations in the tree network. The ranksin this example are assigned as integers beginning with 0 assigned tothe root tasks or root node (202), 1 assigned to the first node in thesecond layer of the tree, 2 assigned to the second node in the secondlayer of the tree, 3 assigned to the first node in the third layer ofthe tree, 4 assigned to the second node in the third layer of the tree,and so on. For ease of illustration, only the ranks of the first threelayers of the tree are shown here, but all compute nodes in the treenetwork are assigned a unique rank.

For further explanation, FIG. 6 sets forth a flow chart illustrating anexample method for collective operation protocol selection in a parallelcomputer according to embodiments of the present invention. The methodof FIG. 6 is carried out in a parallel computer that includes aplurality of compute nodes similar, for example, tot the parallelcomputer (100) depicted in FIG. 1.

The method of claim 6 includes calling (602) a collective operation withone or more operating parameters. Calling (602) a collective operationwith one or more operating parameters may be carried out by executing afunction call to a function provided by a parallel communicationslibrary, the function representing a particular type of collectiveoperation. Examples of collective operations include reduce operations,broadcast operations, gather operations, and the like.

Each collective operation may be carried out in a variety of ways. Eachway of carrying out a collective operation is referred to as a protocol.That is, each collective operation may include a plurality of protocols,with each protocol configured to effect, or execute, the collectiveoperation. To that end, the method of FIG. 6 includes selecting (604)one of a plurality of protocols for executing the collective operation.Such selection (604) is an iterative process, carried out once for eachprospective protocol of the collective operation beginning with a firstprospective protocol and ending when a prospective protocol aprospective protocol meets predetermined performance criteria.

To that end, the method of FIG. 6 includes providing (606), to aprotocol performance function for the prospective protocol, theoperating parameters of the collective operation. Providing (606) theoperating parameters of the collective operation to a performancefunction for the prospective protocol may be carried out by passing theoperating parameters as parameters of a function call to the performancefunction. Consider, the following example function call to a performancefunction for a first prospective protocol of a reduce collectiveoperation:

-   -   bool ProtocolFit=(*Perf_Func_Reduce_Protocol1) (MsgSize,        MsgType);

In the example function call above, the pointer*Perf_Func_Reduce_Protocol1 is a pointer to the performance function ofthe first protocol of a reduce operation. Such a pointer may be storedin metadata associated with and describing the prospective protocol. Theparallel communications library carrying out the selection (604) ofprotocol may retrieve the function pointer from the protocol's metadatato make the function call.

The parameters passed to the performance function include MsgSize—themessage size of the messages being passed in the reduce operation—andMsgType—the type of message being passed during the reduce operation. Inthis example, MsgSize and MsgType are the same operating parameters ofthe collective operation itself. The return of the performance functionis a Boolean value stored as a variable ‘ProtocolFit.’ A true value ofProtocolFit indicates that the protocol meets the predefined performancecriteria for the collective operation and parameter set and a falsevalue of ProtocolFit indicates that the protocol does not meet thepredefined performance criteria for the collective operation andparameter set.

The performance function determines whether to return a true or falsevalue by determining (608) whether the prospective protocol meetspredefined performance criteria for the operating parameters. Predefinedperformance criteria may be any criteria representing a preferredminimum performance level of a particular protocol. Examples of types ofperformance which may be used is criteria include time of execution ofthe collective operation, network bandwidth utilization in effecting thecollective operation, memory resource utilization in effecting thecollective operation, processor resource utilization in effecting thecollective operation, and so on.

A value of the predefined performance criteria may be provided to theperformance function as a parameter of the function call to theperformance criteria. That is, each collective operation, or eachinstance of each collective operation, may have a separate, differentperformance criteria to meet for protocol selection. Alternatively, thepredefined performance criteria may be a single, globally accessiblevalue, available to performance functions of all protocols of allcollective operations.

In the method of FIG. 6, determining (608) whether the prospectiveprotocol meets predefined performance criteria for the operatingparameters includes evaluating (610), with the operating parameters, apredefined performance fit equation for the prospective protocol. Apredefined performance fit equation is an equation defining a measure ofperformance of a protocol over a range of different operation parametersets. The fit equation may be an equation established through regressionanalysis—an approximation of actual performance. Evaluating (610) such afit equation in the method of FIG. 6 includes calculating (612) ameasure of performance of the prospective protocol for the operatingparameters.

Determining (608) whether the prospective protocol meets predefinedperformance criteria for the operating parameters continues bydetermining (614) whether the calculated measure of performance isgreater than a predefined minimum performance threshold. The predefinedminimum performance threshold is a value specified by the predeterminedperformance criteria. That is, in most embodiments, the predeterminedperformance criteria is the predefined minimum performance threshold.

If the calculated measure of performance is not greater than thepredefined minimum performance threshold the method of FIG. 6 continues(616) with another prospective protocol in another iteration of theselection (604) process. If the calculated measure of performance isgreater than the predefined minimum performance threshold, the method ofFIG. 6 continues by selecting (618) the prospective protocol as theprotocol for executing the collective operation and executing (620) thecollective operation with the selected protocol.

For further explanation, FIG. 7 sets forth a flow chart illustrating afurther example method for collective operation protocol selection in aparallel computer according to embodiments of the present invention. Theexample method of FIG. 7 is similar to the example method of FIG. 6 asit also includes: calling (602) a collective operation with operatingparameters; selecting (604) a protocol, including: providing (606) theoperating parameters to a protocol performance function, determining(608) whether the prospective protocol meets predefined performancecriteria, evaluating (610), a predefined performance fit equation,calculating (612) a measure of performance, selecting (618) theprospective protocol only if the calculated measure of performance isgreater than a predefined minimum performance threshold; and executing(620) the collective operation with the selected protocol.

The method of FIG. 7 differs from the method of FIG. 6, however, in thatthe method of FIG. 7 includes a caching process of protocol performancedeterminations carried out prior to actual protocol selection—that is,prior to a collective operation being called. Said another way, thecaching process in the method of FIG. 7 is carried out prior to protocolselection (604), for one or more sets of operating parameters and one ormore prospective protocols of the collective operation. For each set ofoperating parameters and each prospective protocol, the method of FIG. 7includes determining (702) whether the prospective protocol meetspredetermined performance criteria. During establishment (704) of anoperational group of compute nodes, the method of FIG. 7 includescaching (708) each determination of a prospective protocol meeting thepredetermined performance criteria, and not caching (708) determinationof a prospective protocol that does not meet the predeterminedperformance criteria. Consider as an example that a user, prior toestablishing (704) an operation group of compute nodes, initiates thecaching process for a single collective operation—a reduceoperation—with ten sets of operating parameters. For each protocol ofthe reduce operation and for each of the ten sets of operatingparameters, the caching process of FIG. 7 will determine whether theprotocol meets predefined performance criteria for the set of operatingparameters. For each positive determination—a determination that aprotocol does meet the predefined performance criteria for a particularset of operating parameters—the caching process will cache thedetermination upon establishing (704) an operational group of computenodes. Caching determinations may be carried out various ways including,for example, by storing each determination in memory and inserting apointer to the determination in a corresponding protocol's metadata,inserting the determinations in a table or other data structure storedin a well known memory location, and so on as will occur to readers ofskill in the art.

Once an operational group of compute nodes is established (704) andpositive determinations of protocols of collective operations meetingperformance criteria have been cached, the process for selecting (604) aprotocol for executing the collective operation includes determining(710), for the operating parameters of the collective operation, whetherthere is a cached determination of a prospective protocol meeting thepredetermined performance criteria. If there is a cached determinationof a prospective protocol meeting the predetermined performancecriteria, the selection (604) selects (712) the prospective protocol asthe protocol for executing the collective operation, without calculating(612) a measure of performance of the prospective protocol for theoperating parameter during protocol selection. That is, rather thancompleting iteration upon iteration of providing (606) operatingparameters to a performance function, evaluating the performancefunction, calculating a measure of performance, and so on, the method ofFIG. 7 includes selecting a protocol based on a cacheddetermination—bypassing the iterations. Caching determinations as setforth here in FIG. 7 may increase speed and efficiency of selecting(604) a protocol for executing a collective operation by bypassing oneor more iterations, when a cached determination is available.

For further explanation, FIG. 8 sets forth a flow chart illustrating afurther example method for collective operation protocol selection in aparallel computer according to embodiments of the present invention. Theexample method of FIG. 8 is similar to the example method of FIG. 6 asit also includes: calling (602) a collective operation with operatingparameters; selecting (604) a protocol, including: providing (606) theoperating parameters to a protocol performance function, determining(608) whether the prospective protocol meets predefined performancecriteria, evaluating (610), a predefined performance fit equation,calculating (612) a measure of performance, selecting (618) theprospective protocol only if the calculated measure of performance isgreater than a predefined minimum performance threshold; and executing(620) the collective operation with the selected protocol.

The method of FIG. 8 differs from the method of FIG. 6, however, in themethod of FIG. 8 includes establishing (802), for each protocol of acollective operation, a predefined performance fit equation. In themethod of FIG. 8, establishing (802) a predefined performance fitequation includes executing (804) the protocol once for each of a numberof sets of operating parameters, recording (806), for each execution, aperformance measurement, and calculating (808) a fit equation for therecorded performance measurements. Calculating (808) a fit equation forthe recorded performance measurements may be carried out in a variety ofways including for example, by calculating a linear approximation fitequation, a cubic approximation fit equation, and a quarticapproximation fit equation. That regression analysis may be performed tocalculate a fit equation.

Readers of skill in the art will recognize that such approximationsthrough fit equations may be useful when performance of a particularprotocol is somewhat variable. By contrast, in some situationsperformance of a particular protocol may be known exactly. That is, theperformance of some protocols may be deterministic in nature. In such anembodiment, calculating (808) a fit equation for the recordedperformance measurements may include calculating an exact function forall possible operating parameters.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readabletransmission medium or a computer readable storage medium. A computerreadable storage medium may be, for example, but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable transmission medium may include a propagated datasignal with computer readable program code embodied therein, forexample, in baseband or as part of a carrier wave. Such a propagatedsignal may take any of a variety of forms, including, but not limitedto, electro-magnetic, optical, or any suitable combination thereof. Acomputer readable transmission medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

What is claimed is:
 1. An apparatus for collective operation protocolselection in a parallel computer, the parallel computer comprising aplurality of compute nodes, the apparatus comprising a computerprocessor, a computer memory operatively coupled to the computerprocessor, the computer memory having disposed within it computerprogram instructions that, when executed by the computer processor,cause the apparatus to carry out the steps of: calling a collectiveoperation with one or more operating parameters; selecting one of aplurality of protocols that define execution of the collectiveoperation, including, iteratively, for each protocol beginning with afirst prospective protocol until a prospective protocol meetspredetermined performance criteria: providing, to a protocol performancefunction for the prospective protocol, the operating parameters of thecollective operation; determining, by the performance function, whetherthe prospective protocol meets predefined performance criteria for theoperating parameters, including evaluating, with the operatingparameters, a predefined performance fit equation for the prospectiveprotocol, calculating a measure of performance of the prospectiveprotocol for the operating parameters, and determining that theprospective protocol meets predetermined performance criteria; andselecting the prospective protocol as the protocol for executing thecollective operation only if the calculated measure of performance isgreater than a predefined minimum performance threshold; and executingthe collective operation with the selected protocol.
 2. The apparatus ofclaim 1 wherein: each protocol of the collective operation is associatedwith metadata, the metadata for each collective operation including apointer to the protocol's performance function; and providing, to aprotocol performance function for the prospective protocol, theoperating parameters of the collective operation further comprisesretrieving, from the prospective protocol's metadata, the pointer to theprospective protocol's performance function.
 3. The apparatus of claim 1further comprising computer program instructions that, when executed bythe computer processor, cause the apparatus to carry out the steps of:prior to protocol selection, for one or more sets of operatingparameters and one or more prospective protocols of the collectiveoperation: determining whether the prospective protocol meetspredetermined performance criteria; and caching each determination of aprospective protocol meeting the predetermined performance criteria uponestablishment of an operational group of the compute nodes, whereinselecting one of a plurality of protocols for executing the collectiveoperation further comprises: determining, for the operating parametersof the collective operation, whether there is a cached determination ofa prospective protocol meeting the predetermined performance criteria;and if there is a cached determination of a prospective protocol meetingthe predetermined performance criteria, selecting the prospectiveprotocol as the protocol for executing the collective operation, withoutcalculating a measure of performance of the prospective protocol for theoperating parameter during protocol selection.
 4. The apparatus of claim1 further comprising computer program instructions that, when executedby the computer processor, cause the apparatus to carry out the stepsof: establishing, for each protocol of the collective operation, apredefined performance fit equation, including: executing the protocolonce for each of a plurality of sets of operating parameters; recording,for each execution, a performance measurement; and calculating a fitequation for the recorded performance measurements.
 5. The apparatus ofclaim 4 wherein calculating a fit equation for the recorded performancemeasurements further comprises calculating one of: a linearapproximation fit equation; a cubic approximation fit equation; and aquartic approximation fit equation.
 6. The apparatus of claim 4 whereincalculating a fit equation for the recorded performance measurementsfurther comprises calculating an exact function for all possibleoperating parameters.
 7. A computer program product for collectiveoperation protocol selection in a parallel computer, the parallelcomputer comprising a plurality of compute nodes, the computer programproduct disposed upon a computer readable medium that is not a signalmedium, the computer program product comprising computer programinstructions that, when executed, cause a computer to carry out thesteps of: calling a collective operation with one or more operatingparameters; selecting one of a plurality of protocols that defineexecution of the collective operation, including, iteratively, for eachprotocol beginning with a first prospective protocol until a prospectiveprotocol meets predetermined performance criteria: providing, to aprotocol performance function for the prospective protocol, theoperating parameters of the collective operation; determining, by theperformance function, whether the prospective protocol meets predefinedperformance criteria for the operating parameters, including evaluating,with the operating parameters, a predefined performance fit equation forthe prospective protocol, calculating a measure of performance of theprospective protocol for the operating parameters, and determining thatthe prospective protocol meets predetermined performance criteria; andselecting the prospective protocol as the protocol for executing thecollective operation only if the calculated measure of performance isgreater than a predefined minimum performance threshold; and executingthe collective operation with the selected protocol.
 8. The computerprogram product of claim 7 wherein: each protocol of the collectiveoperation is associated with metadata, the metadata for each collectiveoperation including a pointer to the protocol's performance function;and providing, to a protocol performance function for the prospectiveprotocol, the operating parameters of the collective operation furthercomprises retrieving, from the prospective protocol's metadata, thepointer to the prospective protocol's performance function.
 9. Thecomputer program product of claim 7 further comprising computer programinstructions that, when executed, cause the computer to carry out thesteps of: prior to protocol selection, for one or more sets of operatingparameters and one or more prospective protocols of the collectiveoperation: determining whether the prospective protocol meetspredetermined performance criteria; and caching each determination of aprospective protocol meeting the predetermined performance criteria uponestablishment of an operational group of the compute nodes, whereinselecting one of a plurality of protocols for executing the collectiveoperation further comprises: determining, for the operating parametersof the collective operation, whether there is a cached determination ofa prospective protocol meeting the predetermined performance criteria;and if there is a cached determination of a prospective protocol meetingthe predetermined performance criteria, selecting the prospectiveprotocol as the protocol for executing the collective operation, withoutcalculating a measure of performance of the prospective protocol for theoperating parameter during protocol selection.
 10. The computer programproduct of claim 7 further comprising computer program instructionsthat, when executed, cause the computer to carry out the steps of:establishing, for each protocol of the collective operation, apredefined performance fit equation, including: executing the protocolonce for each of a plurality of sets of operating parameters; recording,for each execution, a performance measurement; and calculating a fitequation for the recorded performance measurements.
 11. The computerprogram product of claim 10 wherein calculating a fit equation for therecorded performance measurements further comprises calculating one of:a linear approximation fit equation; a cubic approximation fit equation;and a quartic approximation fit equation.
 12. The computer programproduct of claim 10 wherein calculating a fit equation for the recordedperformance measurements further comprises calculating an exact functionfor all possible operating parameters.